Table Of ContentAgent-BasedModelsinEconomics
Incontrasttomainstreameconomics,complexitytheoryconceivestheeconomyasa
complexsystemofheterogeneousinteractingagentscharacterisedbylimited
informationandboundedrationality.Agent-basedmodels(ABMs)aretheanalytical
andcomputationaltoolsdevelopedbytheproponentsofthisemergingmethodology.
Aimedatstudentsandscholarsofcontemporaryeconomics,thisbookincludesa
comprehensivetoolkitforagent-basedcomputationaleconomics,nowquickly
becomingthenewwaytostudyevolvingeconomicsystems.Leadingscholarsinthe
fieldexplainhowABMscanbeappliedfruitfullytomanyreal-worldeconomic
examples,andrepresentagreatadvancementovermainstreamapproaches.Theessays
discussthemethodologicalbasesofagent-basedapproachesanddemonstrate
step-by-stephowtobuild,simulateandanalyseABMs,andhowtovalidatetheir
outputsempiricallyusingthedata.Thecontributorsalsopresentawidesetofmodel
applicationstokeyeconomictopics,includingthebusinesscycle,labourmarketsand
economicgrowth.
domenico delli gattiisEconomicsProfessoratCatholicUniversity,Milan,
DepartmentofEconomicsandFinance.HeisDirectoroftheComplexityLabin
Economics.Hisresearchinterestsfocusontheroleoffinancialfactors(firms’and
banks’financialfragility)inbusinessfluctuations.TogetherwithMauroGallegatihe
hasprovidedimportantcontributionstoagentbasedmacroeconomics(e.g.,thebook
MacroeconomicsfromtheBottomUp).Hehaspublishedextensivelyinhighranking
journalsandiseditoroftheJournalofEconomicInteractionandCoordination.
giorgio fagioloisProfessorofEconomicsattheInstituteofEconomics,
ScuolaSuperioreSant’Anna,Pisa.Hisresearchinterestsincludeagent-based
computationaleconomics;empiricsandtheoryofeconomicnetworks;andthe
statisticalpropertiesofmicroeconomicandmacroeconomicdynamics.Hispapers
havebeenpublishedinnumerousjournalsincludingScience,theJournalofEconomic
Geography,andtheJournalofAppliedEconometrics.
mauro gallegatiisProfessorofEconomicsattheUniversita`Politecnicadelle
Marche,Ancona,andhehasbeenavisitingprofessorinseveralUniversities,
includingStanford,MITandColumbia.Hehaspublishedjournalpapersinnumerous
subjectareasincludingagentbasedeconomics,complexity,economichistory,
nonlinearmathematics,andeconophysics,andhesitsontheeditorialboardofseveral
economicjournalsandbookseries.
matteo richiardiisSeniorResearchOfficerattheInstituteforNewEconomic
Thinking,MartinOxfordSchool,UniversityofOxford;anassistantprofessoratthe
UniversityofTorino,anassociatememberofNuffieldCollege,Oxford;andan
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affiliateofCollegioCarloAlberto,Torino.Aninternationallyrecognisedscholarin
bothagent-basedanddynamicmicrosimulationmodelling,hehasalsoworkedasa
consultantonlabourmarketpoliciesfortheWorldBank.HeisChiefEditorofthe
InternationalJournalofMicrosimulation,andprojectleaderofJAS-mine,anopen
sourcesimulationplatformfordiscreteeventsimulations(www.jas-mine.net).
alberto russoisAssistantProfessorofEconomicsattheUniversita`Politecnica
delleMarche,Ancona.Hisresearchinterestsincludeagentbasedmodellingand
complexityeconomics,inequalityandmacroeconomicdynamics,andfinancial
fragilityandsystemicrisk.Hehaspublishedinsuchrecognizedjournalsas
InternationalJournalofForecasting,theJournalofEconomicBehaviourand
Organization,andtheJournalofEconomicDynamicsandControl.Healsoservesas
guesteditorandrefereeforseveralinternationaljournals.
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Agent-Based Models in Economics
A Toolkit
Editedby
DOMENICO DELLI GATTI
CatholicUniversityoftheSacredHeart
GIORGIO FAGIOLO
Sant’AnnaSchoolofAdvancedStudies
MAURO GALLEGATI
MarchePolytechnicUniversity
MATTEO RICHIARDI
UniversityofTorino
ALBERTO RUSSO
MarchePolytechnicUniversity
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Informationonthistitle:www.cambridge.org/9781108414999
DOI:10.1017/9781108227278
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Title:Agent-basedmodelsineconomics:atoolkit/editedby
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Toourheterogeneousmostrelevantones.
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Animportantscientificinnovationrarelymakesitswayby
graduallywinningoverandconvertingitsopponents:
Whatdoeshappenisthattheopponentsgraduallydieout.
(MaxPlanck)
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Contents
ListofFigures pagexi
ListofTables xiii
ListofContributors xiv
Preface xvii
1 Introduction 1
1.1 HardTimesforDrPangloss 1
1.2 TheComplexityView 3
1.3 HeterogeneityinaNeoclassicalWorld 4
1.4 Agent-BasedModels(ABMs) 6
1.5 PlanoftheBook 8
2 Agent-BasedComputationalEconomics:What,Why,When 10
2.1 Introduction 10
2.2 FeaturesofAgent-BasedModels 11
2.2.1 ScopeofAgent-BasedModels 12
2.2.2 TheWholeandItsParts 13
2.2.3 TheDualProblemoftheMicro-MacroRelationship 14
2.2.4 Adaptivevs.RationalExpectations 15
2.2.5 AdditionalFeaturesofAgent-BasedModels 17
2.3 TheDevelopmentofACE 20
2.3.1 EvolutionaryRoots 20
2.3.2 TheSantaFePerspective:TheEconomyasan
EvolvingComplexSystem 21
2.3.3 ABModelsasDynamicMicrosimulations 24
2.3.4 TheExperimentalMachine 25
2.4 WhyAgents 27
vii
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viii Contents
2.5 An Ante Litteram Agent-Based Model: Thomas Schelling’s
SegregationModel 29
2.6 Conclusions 32
3 Agent-BasedModelsasRecursiveSystems 33
3.1 Introduction 33
3.2 Discrete-Eventvs.ContinuousSimulationsandthe
ManagementofTime 33
3.3 TheStructureofanABModel 37
3.4 ObtainingResultsinABModels 41
4 Rationality,Behavior,andExpectations 43
4.1 Introduction 43
4.2 Certainty 44
4.3 Uncertainty 45
4.3.1 RiskNeutrality 46
4.3.2 RiskAversion 47
4.3.3 OptimalChoiceinaMulti-PeriodSetting 50
4.4 AdaptationinExpectationFormation 55
4.5 RidingatFullGallopthroughtheHistoryofMacroeconomics 56
4.5.1 TheNeoclassical-KeynesianSynthesis 57
4.5.2 ExpectationsEntertheScene 58
4.5.3 AdaptiveExpectations 59
4.5.4 RationalExpectations 62
4.5.5 TheNewNeoclassicalSynthesis 68
4.6 TheLimitsofRationalExpectations 71
4.7 HeterogeneousExpectations:AVerySimpleIntroduction 72
4.7.1 Heterogeneous-BiasedExpectations 72
4.7.2 AConvenientSpecialCase:TwoTypes 74
4.7.3 HeterogeneousAdaptiveExpectations 76
4.8 HeterogeneousExpectationsinABMs 76
4.9 Conclusions 79
5 Agents’BehaviorandLearning 81
5.1 Introduction 81
5.2 FullandBoundedRationality 82
5.2.1 EmpiricalMicrofoundationsofIndividualBehavior 85
5.2.2 Agents’BehaviorandHeuristics 90
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Contents ix
5.3 Learning 93
5.3.1 IndividualLearning1:StatisticalLearning 95
5.3.2 IndividualLearning2:FitnessLearning 97
5.3.3 SocialLearning 105
5.3.4 Individualvs.SocialLearning 107
5.4 Conclusions 108
6 Interaction 109
6.1 Introduction 109
6.2 ModelingInteractions 110
6.2.1 LocalExogenousInteraction 114
6.2.2 EndogenousInteraction 118
6.3 Networks:BasicConceptsandProperties 125
6.4 StaticandDynamicNetworks 133
6.4.1 StaticNetworks 133
6.4.2 DynamicNetworks 137
6.5 Conclusions 141
7 TheAgent-BasedExperiment 143
7.1 Introduction 143
7.2 Long-RunandTransientEquilibria 144
7.2.1 Definitions 144
7.2.2 UniquenessandMultiplicityofEquilibria 146
7.2.3 ImplicationsofStationarityandErgodicity 150
7.3 SensitivityAnalyisofModelOutput 151
7.3.1 SettingsforSA 152
7.3.2 StrategiesforSA 152
7.3.3 SAandABModelling:SomeApplications 156
7.3.4 A Simple Example: SA on a Bass Diffusion Model
withLocalInteraction 156
7.4 Conclusions 161
8 EmpiricalValidationofAgent-BasedModels 163
8.1 Introduction 163
8.2 TheMethodologicalBasisofEmpiricalValidation 165
8.2.1 Tractabilityvs.Accuracy 166
8.2.2 Instrumentalismvs.Realism 167
8.2.3 Pluralismvs.Apriorism 167
8.2.4 TheIdentificationProblem 168
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x Contents
8.3 InputValidationofAgent-BasedModels 169
8.4 OutputValidationofAgent-BasedModels 172
8.5 QualitativeOutputValidationTechnqiues 176
8.5.1 TheIndirectCalibrationApproach 178
8.5.2 TheHistory-FriendlyApproach 180
9 EstimationofAgent-BasedModels 183
9.1 Introduction 183
9.2 TakingtheModeltotheData 185
9.2.1 ComparingAppleswithApples 185
9.2.2 PreliminaryTests 187
9.2.3 Simulation-BasedEstimation 189
9.2.4 Consistency 191
9.2.5 Calibrationvs.Estimation 192
9.3 SimulatedMinimumDistance 195
9.3.1 TheMethodofSimulatedMoments 195
9.3.2 ErgodicityandanApplicationtoaSimpleABModel 203
9.4 BayesianEstimation 210
9.4.1 EstimatingtheLikelihood 211
9.4.2 SamplingthePosteriorDistribution 214
9.4.3 ApproximateBayesianComputation 216
9.4.4 ABCEstimationoftheSegregationModel 219
9.5 Conclusions 221
10 Epilogue 222
Bibliography 224
Index 240
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